1. Technical Field
Embodiments of the subject matter disclosed herein generally relate to methods and systems for eliminating multiples from seismic data acquired at different times at the same location (4D seismic surveys), thereby achieving a clearer image of real changes occurring in time in the subsurface.
2. Discussion of the Background
A widely used technique for searching for oil or gas is the seismic exploration of subsurface (i.e., geophysical structure). The seismic exploration consists of generating seismic waves directed toward the subsurface, gathering data on reflections of the generated seismic waves at interfaces between layers of the subsurface, and analyzing the data to generate a profile (image) of the geophysical structure, i.e., the layers of the investigated subsurface. The seismic exploration is used both on land and for exploring the subsurface under the sea floor.
As illustrated in FIG. 1, during a marine seismic exploration, a vessel 10 tows an array of seismic receivers 12 provided on cables 14 that form streamers 16. The streamers may be disposed horizontally, i.e., lying at a constant depth Z1 relative to a surface 18 of the ocean. However, the streamers may also be towed slanted or bent. The vessel 10 also tows a seismic source assembly 20 that is configured to generate pressure waves (also known as seismic or acoustic waves), such as the wave 22a. The wave 22a propagates downward toward the sea floor 24 and penetrates the subsurface under the sea floor 24 until eventually reflecting (R) at an interface 26 between layers of the subsurface. The reflected wave 22b then propagates upward until it is detected by a receiver 12. The receiver 12 records data related to the reflected wave 22b (e.g., time of arrival relative to time when the wave 22a was generated, energy, etc). Data gathered by the multiple receivers 12 is then processed for generating an image of the geophysical structure under the sea floor 24 (i.e., the subsurface).
The signal recorded by a seismic receiver varies in time, having energy peaks that may correspond to reflectors between layers. In reality, since the sea floor and the air/water are highly reflective, some of the peaks correspond to multiple reflections or spurious reflections that should be eliminated before the geophysical structure can be correctly imaged. Primary waves suffer only one reflection from an interface between layers of the subsurface. Waves other than primary waves are known as multiples.
For example, as illustrated in FIG. 2, a seismic source 30 produces a wave 32 that splits into a primary wave 34 penetrating inside the subsurface 25 under the sea floor 24, and a multiple wave 36 that travels back toward the sea surface 18. The primary wave 34 is reflected only once at an interface 26 between different layers in the subsurface 25 and travels back to a receiver R 40. The multiple wave 36 also reaches the receiver R 40 at a different time, after being reflected two more times: once at the surface 18 and a second time at the sea floor 24. Thus, as graphically illustrated in FIG. 3 (where the vertical axis represents time and the horizontal axis represents energy), a received signal includes a first energy peak 50 corresponding to the primary wave 34 and a second peak 60 corresponding to the multiple wave 36 that suffered multiple reflections. The columns 52 and 62 illustrate the layers traveled by the primary wave 34 and by the multiple wave 36, respectively, from the source to the receiver. Thus, the primary wave travels through water and a first layer, and it is reflected at an interface between the first layer and a second layer of the subsurface under the sea floor. The multiple wave 36 illustrated in FIGS. 2 and 3 travels only through water. However, other multiples may also travel through the subsurface.
A number of seismic processing methods are currently used to remove the multiples from the seismic data, for example: T-x domain predictive deconvolution, Tau-p deconvolution (linear RADON), parabolic RADON, 2D and 3D surface-related multiple elimination (SRME). Some of these methods consist of modeling multiple reflections in order to subtract (or adaptively subtract) them from seismic data and, thus, to keep only useful seismic data (i.e., the primary waves) to image the subsurface.
In “Attenuation of complex water-bottom multiples by wave-equation-based prediction and subtraction” (by Wiggins, J. W., published in 1988 in Geophysics, 53, 1527-1539) and in “Estimation of multiple scattering by iterative inversion: Part II: Practical aspects and examples” (by Verschuur et al., published in 1997 in Geophysics, 62, 1596-1611), the authors describe wave-equation-based multiple attenuation or data-driven surface-related multiple elimination methods. These methods consist of two steps: (1) multiple prediction followed by (2) adaptive subtraction. The multiple prediction may be model-driven or data-driven. The model-driven wave-equation multiple prediction requires knowledge about the near-surface water velocity model, the water bottom topography, and subsurface velocities. Data-driven surface-related multiple prediction requires that the source and receiver are co-located, the source signatures are consistent, and that the near offset traces are not missing, as described in “An improved adaptive multiple subtraction method” (by Guangkai, M. et al, in 2009 in 79th SEG Annual International Meeting, Expanded Abstract, 3163-3167). Furthermore, variations in the acquisition wavelet, cable feathering, boundary effect, and limited offset range can also introduce time shifts or amplitude artifacts into the predicted multiples. In practice, therefore, the predicted multiples need to be adapted to the data prior to subtraction.
Conventionally, in order to remove multiples from the acquired data, the following steps are executed:                1. sorting the data into an optimal domain (e.g., receiver gather sorting)—subject to testing;        2. predicting multiples with a given demultiple method—subject to testing; and        3. adaptively subtracting one or more multiple models from the data in the domain—subject to testing.        
After removing the multiples from the acquired data, the remaining peaks in the data are estimated primaries.
In reservoir surveillance, at least two seismic data vintages representing time-lapse seismic data for the same subsurface are gathered. It is desirable for the process of removing the multiples to be optimum for all seismic data vintages. The time-lapse acquisition of seismic data adds complexity to the problem of extracting multiples because the seismic data vintages are gathered potentially with different data acquisition configurations, different offset ranges and cable length, non-identical sources and departures from the ideal geometry (i.e., feathering), etc. Conventionally, in order to handle this complexity, adaptive non-stationary operators are designed to subtract the predicted multiples for each seismic data vintage individually. However, this approach neglects the key time-lapse product which is, of course, the 4D difference; that is, the real changes in the substructure that have occurred in the time interval(s) between the times when the seismic data vintages were acquired.
The complexity of extracting multiples increases factorially with the number of seismic data vintages. For example, with three seismic data vintages, there are six quantities (three seismic data vintages and three differences) to be optimized. For four seismic data vintages, there are ten quantities (four seismic data vintages and six differences) to be optimized. Similar problems arise in other areas of time-lapse processing, such as multi-vintage time shift estimation (as described in “Simultaneous multi-vintage time shift estimation” by Zabihi Naeini et al., published in 2009 in Geophysics, 74(5), V109-V121) or time-lapse residual matching (as described in “Simultaneous multi-vintage multi-parameter time lapse matching” by Zabihi Naeini et al., published in 2010 in 72nd EAGE Conference & Exhibition, Expanded Abstract, B038).
When multiple seismic data vintages are analyzed, one (typically the first in time) is considered “base” and the other seismic data vintage(s), acquired later in time, are “monitor(s).” Conventionally, as illustrated in FIG. 4, each seismic data vintage is processed separately (the base at 410-420-430, monitor 1 at 440-450-460 and monitor N at 470-480-490) and independently to optimally remove the multiples in each data set (i.e., the base and each of the monitors separately), and the remaining estimated primaries are then compared to identify 4-D differences at 500. The purpose of analyzing multiple seismic data vintages is to identify the actual changes (e.g., due to exploitation of the reservoir) in the subsurface. These actual changes are essentially changes in the primaries. As illustrated in FIG. 5, conventional processing (e.g., separate processing of the base and the monitor) yields “leaky” differences between multiples 510, which are spurious effects that may obscure the real differences in the primaries. The leakage may appear because for both data sets (i.e., the base and the monitor), the adaptive subtraction may be suboptimal. This leaves residual (small) multiple energy in the data. The residual multiple energies for both data sets remain in the 4D difference because they are different (i.e., not the same residual multiple energy).
As illustrated in FIG. 5, although there were no substantive changes in the estimated primaries between the base and the monitor, there appear to be some changes due to the “leaky” subtraction of the multiples.
Thus, a method optimizing simultaneously (1) extracting predicted multiples from all the time-lapse vintages and (2) minimizing the 4D differences between corresponding multiples of different seismic data vintages is desirable.